


from string import ascii_uppercase

snake_case				:			Tuple	     = {char: i for i, char in enumerate(ascii_uppercase)}
snake_case				:			List[str]	     = dict(enumerate(ascii_uppercase))






def 				__lowercase  (					__lowerCAmelCase				:							List[str]   ,       __lowerCAmelCase				:							Union[str, Any]  ):
							a__			 =	len(__lowerCAmelCase  )
							a__			 =	0
							while True:
														if x == i:
																					a__			 =	0
														if len(__lowerCAmelCase  ) == len(__lowerCAmelCase  ):
																					break
														key += key[i]
														i += 1
							return key






def 				__lowercase  (					__lowerCAmelCase				:							Union[str, Any]   ,       __lowerCAmelCase				:							List[Any]  ):
							a__			 =	""""""
							a__			 =	0
							for letter in message:
														if letter == " ":
																					cipher_text += " "
														else:
																					a__			 =	(dicta[letter] - dicta[key_new[i]]) % 2_6
																					i += 1
																					cipher_text += dicta[x]
							return cipher_text






def 				__lowercase  (					__lowerCAmelCase				:							Optional[Any]   ,       __lowerCAmelCase				:							Optional[int]  ):
							a__			 =	""""""
							a__			 =	0
							for letter in cipher_text:
														if letter == " ":
																					or_txt += " "
														else:
																					a__			 =	(dicta[letter] + dicta[key_new[i]] + 2_6) % 2_6
																					i += 1
																					or_txt += dicta[x]
							return or_txt






def 				__lowercase  (					):
							a__			 =	"""THE GERMAN ATTACK"""
							a__			 =	"""SECRET"""
							a__			 =	generate_key(__lowerCAmelCase   ,       __lowerCAmelCase  )
							a__			 =	cipher_text(__lowerCAmelCase   ,       __lowerCAmelCase  )
							print(F'Encrypted Text = {s}'  )
							print(F'Original Text = {original_text(__lowerCAmelCase   ,       __lowerCAmelCase  )}'  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :			Union[str, Any]    =	{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			List[Any]    =	[
							    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TapasForMaskedLM""",
							    """TapasForQuestionAnswering""",
							    """TapasForSequenceClassification""",
							    """TapasModel""",
							    """TapasPreTrainedModel""",
							    """load_tf_weights_in_tapas""",
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Optional[Any]    =	[
							    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFTapasForMaskedLM""",
							    """TFTapasForQuestionAnswering""",
							    """TFTapasForSequenceClassification""",
							    """TFTapasModel""",
							    """TFTapasPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Any    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






